Observer: Keeping System Models from Becoming Obsolete (CMU-PDL-07-101)

To be effective for automation, in practice, system models used for performance prediction and behavior checking must be robust. They must be able to cope with component upgrades, misconfigurations, and workload-system interactions that were not anticipated. This paper promotes making models self-evolving, such that they continuously evaluate their accuracy and adjust their predictions accordingly. Such self-evaluation also enables confidence values to be provided with predictions, including identification of situations where no trustworthy prediction can be produced. With a combination of expectation-based and observation based techniques, we believe that such self-evolving models can be achieved and used as a robust foundation for tuning, problem diagnosis, capacity planning, and administration tasks.